"""simple docstring"""



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
   from sagemaker import Session, TrainingJobAnalytics
   from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""",						"""False"""       )       ) is not True,						reason="""Skipping test because should only be run when releasing minor transformers version""",						)
@pytest.mark.usefixtures("""sm_env"""       )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]       )
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        if self.framework == "pytorch":
               subprocess.run(
                   F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()    ,		encoding="utf-8"    ,		check=_lowerCamelCase    ,		)
        assert hasattr(self    ,		"env"						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
        # distributed data settings
        __a       =    {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

        # creates estimator
        return HuggingFace(
            entry_point=self.script    ,		source_dir=self.env.test_path    ,		role=self.env.role    ,		image_uri=self.env.image_uri    ,		base_job_name=_lowerCamelCase    ,		instance_count=_lowerCamelCase    ,		instance_type=self.instance_type    ,		debugger_hook_config=_lowerCamelCase    ,		hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}    ,		metric_definitions=self.env.metric_definitions    ,		distribution=_lowerCamelCase    ,		py_version="py36"    ,		)


 def 						a__   ( self    ,		lowerCamelCase						):
        TrainingJobAnalytics(_lowerCamelCase						).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"						)






 @parameterized.expand([(2,)]						)
 def 						a__   ( self    ,		lowerCamelCase						):
        # create estimator
        __a       =    self.create_estimator(_lowerCamelCase						)

        # run training
        estimator.fit()

        # result dataframe
        __a       =    TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

        # extract kpis
        __a       =    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
        __a       =    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        __a       =    (
            Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"    ,		999999						)
        )

        # assert kpis
        assert train_runtime <= self.results["train_runtime"]
        assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy						)
        assert all(t <= self.results["eval_loss"] for t in eval_loss						)

        # dump tests result into json file to share in PR
        with open(F"{estimator.latest_training_job.name}.json"    ,		"w"						) as outfile:
               json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}    ,		_lowerCamelCase						)

"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square(__lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												# BASE CASE
												if row >= rows or col >= cols:
																		return 0

												lowerCamelCase__   =update_area_of_max_square(__lowerCAmelCase  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						update_area_of_max_square(0  ,       0							)
						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square_using_dp_array(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												if row >= rows or col >= cols:
																		return 0
												if dp_array[row][col] != -1:
																		return dp_array[row][col]

												lowerCamelCase__   =update_area_of_max_square_using_dp_array(__lowerCAmelCase  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		lowerCamelCase__   =sub_problem_sol
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						lowerCamelCase__   =[[-1] * cols for _ in range(__lowerCAmelCase							)]
						update_area_of_max_square_using_dp_array(0  ,       0  ,       __lowerCAmelCase							)

						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[[0] * (cols + 1) for _ in range(rows + 1							)]
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =dp_array[row][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(dp_array[row][col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0

						return largest_square_area





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =current_row[col + 1]
																		lowerCamelCase__   =next_row[col + 1]
																		lowerCamelCase__   =next_row[col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(current_row[col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0
												lowerCamelCase__   =current_row

						return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


